Method and apparatus for performing motor-fault detection via convolutional neural networks

ABSTRACT

A method and apparatus may include receiving a signal from a motor. The signal is received while the motor is operating. The method also includes performing a pre-processing of the signal. The method also includes inputting the signal to a 1D convolutional neural network. The method also includes detecting a fault of the motor based on the output of the neural network.

BACKGROUND Field

Certain embodiments of the present invention relate to performingmotor-fault detection via convolutional neural networks.

Description of the Related Art

A convolutional neural network generally refers to a type offeed-forward artificial neural network. A feed-forward neural networkmay be considered to be a network where the information is transmittedthrough the network in a single direction. For example, information maybe transmitted to the network via input nodes, through hidden nodes (ifapplicable), and to output nodes (if applicable). Backpropagation isgenerally considered to be a method of training neural networks wherethe networks can learn the appropriate internal representations/weightsto allow the networks to learn correspondences between inputs andoutputs.

SUMMARY

According to a first embodiment, a method can include receiving a signalfrom a motor. The signal is received while the motor is operating. Themethod can also include performing a pre-processing of the signal. Themethod can also include inputting the signal to a 1D convolutionalneural network. The method can also include detecting a fault of themotor based on an output of the neural network.

In the method of the first embodiment, receiving the signal includesreceiving the signal from an induction motor.

In the method of the first embodiment, performing the pre-processing ofthe signal includes at least one of down-sampling the signal, filteringthe signal, and normalizing the signal.

In the method of the first embodiment, receiving the signal includesreceiving a motor current signal.

In the method of the first embodiment, the 1D convolution neural networkcan be configured to perform a plurality of back-propagation iterations,and to update weights and bias sensitivities of the 1D convolutionneural network for each back-propagation iteration.

In the method of the first embodiment, detecting the fault of the motorincludes detecting a bearing fault of the motor.

According to a second embodiment, an apparatus can include at least oneprocessor. The apparatus can also include at least one memory includingcomputer program code. The at least one memory and the computer programcode can be configured, with the at least one processor, to cause theapparatus at least to receive a signal from a motor. The signal isreceived while the motor is operating. The apparatus is also caused toperform a pre-processing of the signal. The apparatus is also caused toinput the signal to a 1D convolutional neural network. The apparatus isalso caused to detect a fault of the motor based on an output of theneural network.

In the apparatus of the second embodiment, receiving the signal includesreceiving the signal from an induction motor.

In the apparatus of the second embodiment, performing the pre-processingof the signal includes at least one of down-sampling the signal,filtering the signal, and normalizing the signal.

In the apparatus of the second embodiment, receiving the signal includesreceiving a motor current signal.

In the apparatus of the second embodiment, the 1D convolution neuralnetwork is configured to perform a plurality of back-propagationiterations, and to update weights and bias sensitivities of the 1Dconvolution neural network for each back-propagation iteration.

In the apparatus of the second embodiment, detecting the fault of themotor includes detecting a bearing fault of the motor.

According to a third embodiment, a computer program product can beembodied on a non-transitory computer readable medium. The computerreadable medium has instructions stored thereon that, when executed by acomputer, causes the computer to perform a method. The method includesreceiving a signal from a motor. The signal is received while the motoris operating. The method also includes performing a pre-processing ofthe signal. The method also includes inputting the signal to a 1Dconvolutional neural network. The method also includes detecting a faultof the motor based on an output of the neural network.

In the computer program product of the third embodiment, receiving thesignal includes receiving the signal from an induction motor.

In the computer program product of the third embodiment, performing thepre-processing of the signal includes at least one of down-sampling thesignal, filtering the signal, and normalizing the signal.

In the computer program product of the third embodiment, receiving thesignal comprises receiving a motor current signal.

In the computer program product of the third embodiment, the 1Dconvolution neural network is configured to perform a plurality ofback-propagation iterations, and to update weights and biassensitivities of the 1D convolution neural network for eachback-propagation iteration.

In the computer program product of the third embodiment, detecting thefault of the motor comprises detecting a bearing fault of the motor.

According to a fourth embodiment, an apparatus can include receivingmeans to receive a signal from a motor. The signal is received while themotor is operating. The apparatus also includes performing means toperform a pre-processing of the signal. The apparatus also includesinputting means to input the signal to a 1D convolutional neuralnetwork. The apparatus also includes detecting means to detect a faultof the motor based on an output of the neural network.

BRIEF DESCRIPTION OF THE DRAWINGS

For proper understanding of the invention, reference should be made tothe accompanying drawings, wherein:

FIG. 1 illustrates a typical ball-bearing geometry.

FIG. 2 illustrates performing real-time motor-condition monitoring inaccordance with certain embodiments of the present invention.

FIG. 3 illustrates a healthy motor current signal, as well as itsamplitude spectrum, before and after performing preprocessing, inaccordance with certain embodiments of the present invention.

FIG. 4 illustrates a faulty motor current signal, and its amplitudespectrum, before and after preprocessing, in accordance with certainembodiments of the present invention.

FIG. 5 illustrates convolution layers of an adaptive convolution neuralnetwork (CNN) configuration, in accordance with certain embodiments ofthe present invention.

FIG. 6 illustrates a backpropagation algorithm of certain embodiments ofthe present invention.

FIG. 7 illustrates a flowchart of a method in accordance with certainembodiments of the invention.

FIG. 8 illustrates an apparatus in accordance with certain embodimentsof the invention.

FIG. 9 illustrates an apparatus in accordance with certain embodimentsof the invention.

DETAILED DESCRIPTION

Certain embodiments of the present invention relate to performingmotor-fault detection by using convolutional neural networks (CNNs). Theability to perform early detection of faults within motors is consideredto be a very desirable/essential function, and Artificial NeuralNetworks (ANNs) are widely used for performing such early detection.

Motor-fault detection and diagnosis methods can generally be dividedinto two categories: model-based methods and signal-based methods.Model-based methods use mathematical models to describe the normaloperating conditions of motors (such as induction motors, for example).

Signal-based methods generally perform two distinct operations: (1)feature extraction, and (2) classification of the extracted features.Although the previous approaches are generally able to perform accuratedetection of anomalies/faults, most of the previous approaches have toutilize a variety of different features and/or classifiers (to performfeature extraction and classification) for the different/various typesof motor data. For each specific type of signal/motor data, theimportant step of choosing the appropriate feature to characterize fromwithin the signal/motor data can be a very difficult step to perform.

The fixed and manually-configured/hand-crafted features (which are usedto perform feature extraction) of the previous approaches may notaccurately represent the characteristics of every type of motor currentsignal in an optimal way, and thus the previous approaches generallycannot accomplish a general solution that can be used for any motordata. In other words, because, for each particular type of signal (i.e.,motor current data), it is unknown which feature extraction is the bestchoice, the previous approaches struggle with extracting the properfeature. Furthermore, feature extraction usually turns out to be acomputationally-costly operation, which eventually may hinder the usageof such methods in applications that monitor motors in real-time.

Certain embodiments of the present invention address the above-describeddrawbacks and limitations of using Convolutional Neural Networks (CNNs)in accordance with the previous approaches. Certain embodiments aredirected to a fast and accurate motor-condition monitoring system, withan adaptive implementation of 1D CNNs to inherently combine the featureextraction and classification functions into a single learning body.Certain embodiments of the present invention may directly classify inputsignal samples acquired from the motor current which can be the linecurrent data of the motor captured at 128 points per cycle using anindustrial circuit monitor. As such, certain embodiments may be directedto an efficient system, in terms of speed, that may be used in real-timeapplications.

As mentioned above, due to the CNN's ability to learn how to extract theoptimal features, with the proper training, certain embodiments of thepresent invention can perform classification and fault detectionaccurately.

The main sources of failure within induction machines/motors includeboth mechanical-type failures that are caused by faults within bearingsfaults, as well as electrical-type failures that are caused byinsulation or winding faults. Bearing faults are, by far, thepredominant cause of all motor failures. Bearing faults are the mostdifficult type of fault to detect, while also being the least expensivetype of fault to fix when these types of faults are detected earlyenough. As such, certain embodiments of the present invention aredirected to enabling the early detection of bearing faults.

Bearing faults generally result from mechanical defects, and bearingfaults generally cause vibration at fault-related frequencies. Thefault-related frequencies can be determined based upon a known bearinggeometry and a known shaft speed. FIG. 1 illustrates a typicalball-bearing geometry.

The equations that are used for calculating both characteristicvibration frequencies due to the presence of bearing faults and theresulting current frequencies are given as follows:

Outer race defect frequency, f_(OD), the ball passing frequency on theouter race, is given by

$\begin{matrix}{f_{OD} = {\frac{n}{2}{f_{rm}\left( {1 - {\frac{BD}{PD}\cos\mspace{11mu}\phi}} \right)}}} & (1)\end{matrix}$where f_(mn) is the rotor speed in revolutions per second, n is thenumber of balls, and the angle ϕ is the contact angle, which is zero forball bearings.Inner race defect frequency f_(ID), the ball passing frequency on theinner race, is expressed as

$\begin{matrix}{f_{ID} = {\frac{n}{2}{f_{rm}\left( {1 + {\frac{BD}{PD}\cos\mspace{11mu}\phi}} \right)}}} & (2)\end{matrix}$Cage defect frequency f_(CD), caused by irregularity in the rollingelement train, is given by

$\begin{matrix}{f_{CD} = {\frac{1}{2}{f_{rm}\left( {1 - {\frac{BD}{PD}\cos\mspace{11mu}\phi}} \right)}}} & (3)\end{matrix}$Ball defective frequency f_(BD), the ball spin frequency, is given by

$\begin{matrix}{f_{BD} = {\frac{PD}{2{BD}}{f_{rm}\left( {1 - {\left( \frac{BD}{PD} \right)^{2}\cos^{2}\mspace{11mu}\phi}} \right)}}} & (4)\end{matrix}$The mechanical vibration due to a bearing defect may also result in airgap eccentricity. Oscillations in air gap width, in turn, may causevariations in flux density. The variations in flux density affect themachine inductances that produce stator current vibration harmonics. Thecharacteristic current frequencies, f_(CF), due to bearingcharacteristic vibration frequencies can be expressed asf _(CF) =|f _(e) ±mf _(v)|  (5)where f_(e) is the line frequency, m is an integer and f_(v) is thecharacteristic vibration frequency obtained from Eqs. 1-4 above.Detection of the above frequencies (within a motor current signal) bycertain embodiments of the present invention may result in detection ofa fault within the motor.

FIG. 2 illustrates performing real-time motor-condition monitoring inaccordance with certain embodiments of the present invention. Certainembodiments of the present invention receive signals from a motor 210 toidentify potential faults within the motor. For example, certainembodiments may identify potential faults based on the fault-relatedfrequencies described above. Specifically, with certain embodiments, thesignals from the motor are input into a 1D convolutional neural networkclassifier 230. The input signals that are received from the motors canalso undergo a pre-processing step 220 before being input into the 1DCNN classifier 230.

Simple 1D CNNs may be easier to train, as training generally involvesonly a few hundred back-propagation (BP) epochs. As such, with thesimple 1D CNNs, certain embodiments can perform the classification taskwith great speed (generally requiring only few hundreds of 1Dconvolutions). As such, certain embodiments can use the simple 1D CNNsfor performing real-time, and cost-effective, motor fault detectionwithin early fault-alert systems.

With regard to the preprocessing, with certain embodiments, an inputsignal may be filtered by a second-order notch filter in order tosuppress a fundamental frequency for preprocessing. The input signal maybe down-sampled (for example, by a factor of 8) by performing adecimation preceded by an anti-aliasing filtering. The input signal maybe a motor current signal. The decimated signal may then be normalizedproperly to be the input of the 1D CNN classifier. The decimation allowsthe usage of a simpler CNN configuration, which, in turn, improves bothtraining and detection speeds. Finally, the training and test sets maybe normalized to have a zero mean and to have uniform standarddeviation, in order to remove the effect of DC offset and amplitudebiases. Then, the training and test sets can be linearly scaled into a[−1, 1] interval, before being presented to the CNN classifier.

FIG. 3 illustrates a healthy motor current signal, as well as itsamplitude spectrum, before and after performing preprocessing. FIG. 4illustrates a faulty motor current signal, and its amplitude spectrum,before and after preprocessing.

Certain embodiments of the present invention may utilizeback-propagation techniques with the adaptive 1D CNNs. Specifically,certain embodiments can utilize an adaptive 1D CNN configuration inorder to combine feature extraction and learning (fault detection)phases, when receiving the raw motor current signals. The adaptive CNNtopology can allow certain embodiments to work with any input layerdimension.

Furthermore, the proposed adaptive CNN topology of certain embodimentsmay be configured such that hidden neurons of the convolution layers canperform both convolution and sub-sampling operations. FIG. 5 illustratesthe convolution layers of the adaptive CNN configuration, in accordancewith certain embodiments of the present invention.

With certain embodiments of the present invention, the combining of aconvolution and a sub sampling layer may be considered to be the “CNNlayer,” while the remaining layers may be considered to be themultilayer perceptron (MLP) layers. So, the 1D CNNs may include an inputlayer, hidden CNN and MLP layers, and an output layer.

Further, structural differences are visible between the traditional 2Dand the proposed 1D CNNs. One difference is the usage of 1D arraysinstead of 2D matrices for both kernels and feature maps. Accordingly,the 2D matrix manipulations such as 2D convolution (conv2D) and lateralrotation (rot180) have now been replaced by their 1D counterparts,conv1D and reverse. Moreover, the parameters for kernel size andsub-sampling are now scalars, K and ss for 1D CNNs, respectively.However, the MLP layers may be similar to the layers of the 2Dcounterpart and, therefore, certain embodiments may have a traditionalBP formulation. In 1D CNNs, the 1D forward propagation (FP) from aprevious convolution layer, l−1, to the input of a neuron in the currentlayer, l, can be expressed as,

$\begin{matrix}{x_{k}^{l} = {b_{k}^{l} + {\sum\limits_{i = 1}^{N_{i - 1}}{{conv}\; 1{D\left( {w_{ik}^{l - 1},s_{i}^{l - 1}} \right)}}}}} & (6)\end{matrix}$Where x¹ _(k) is the input, b¹ _(k) is a scalar bias of the kth neuronat layer l, and s^(l-1) _(i) is the output of the ith neuron at layerl−1. W^(l-1) _(ik) is the kernel from the ith neuron at layer l−1 to thekth neuron at layer l.The intermediate output of the neuron, y¹ _(k), can then be expressedfrom the input, x¹ _(k), as follows:y _(k) ¹ =f(x _(k) ¹) and s _(k) ¹ =y _(k) ¹ ↓ss  (7)Where s¹ _(k) is the output of the neuron and ↓ ss represents thedown-sampling operation with the factor, ss. The adaptive CNNconfiguration may use the automatic assignment of the sub-samplingfactor of the output CNN layer (the last CNN layer). It may be set tothe size of its input array. For instance, in FIG. 5, assuming that thelayer l+1 is the last CNN layer, then ss=8 automatically because theinput array size is 8. Such a design allows the usage of any number ofCNN layers. This adaptation capability is possible in this CNNconfiguration because the output dimension of the last CNN layer canautomatically be downsized to 1 (scalar), regardless from the nativesubsampling factor parameter that was set in advance for the CNN.The back-propagation (BP) steps of certain embodiments are describedbelow. The BP of the error can start from the output MLP layer. Let l=1and 1=L be the input and output layers, respectively. Also, let NL bethe number of classes in the database. For an input vector p, and itscorresponding target and output vectors,t _(i) ^(p) and [y ₁ ^(L) , . . . ;y _(N) _(L) ^(L)]respectively, the mean-squared error (MSE) in the output layer for theinput p, Ep, can be expressed as follows:

$\begin{matrix}{E_{p} = {{M\; S\; E\mspace{11mu}\left( {t_{i}^{p},\left\lbrack {y_{1}^{L},\ldots\mspace{11mu},y_{N_{L}}^{L}} \right\rbrack} \right)} = {\sum\limits_{i = 1}^{N_{L}}\left( {y_{i}^{L} - t_{i}^{p}} \right)^{2}}}} & (8)\end{matrix}$The objective of the BP is minimize the contributions of networkparameters to this error. Therefore, certain embodiments compute thederivative of the MSE with respect to an individual weight (connected tothat neuron, k) w^(l-1) _(ik), and a bias of the neuron k, b¹ _(k), sothat certain embodiments can perform a gradient descent method tominimize their contributions and hence the overall error in an iterativemanner. Specifically, the delta of the kth neuron at layer l, Δ¹ _(k)will be used to update the bias of that neuron and all weights of theneurons in the previous layer connected to that neuron, as,

$\begin{matrix}{\frac{\partial E}{\partial w_{ik}^{l - 1}} = {{\Delta_{k}^{l}y_{i}^{l - 1}\mspace{14mu}{and}\mspace{14mu}\frac{\partial E}{\partial b_{k}^{l}}} = \Delta_{k}^{l}}} & (9)\end{matrix}$So, from the first MLP layer to the last CNN layer, the regular (scalar)BP is simply performed as,

$\begin{matrix}{\frac{\partial E}{\partial s_{k}^{l}} = {{\Delta\; s_{k}^{l}} = {{\sum\limits_{i = 1}^{N_{i - 1}}{\frac{\partial E}{\partial x_{i}^{l + 1}}\frac{\partial x_{i}^{l + 1}}{\partial s_{k}^{l}}}} = {\sum\limits_{i = 1}^{N_{i - 1}}{\Delta_{i}^{l + 1}w_{ki}^{l}}}}}} & (10)\end{matrix}$Once the first BP is performed from the next layer, l+1, to the currentlayer, l, then certain embodiments can further back-propagate it to theinput delta, Δ¹ _(k). Let zero order up-sampled map be:us_(k) ¹=up(s_(k) ¹), then one can write:

$\begin{matrix}{\Delta_{k}^{I} = {{\frac{\partial E}{\partial y_{k}^{l}}\frac{\partial y_{k}^{l}}{\partial x_{k}^{l}}} = {{\frac{\partial E}{\partial{us}_{k}^{l}}\frac{\partial{us}_{k}^{l}}{\partial y_{k}^{l}}{f^{\prime}\left( x_{k}^{l} \right)}} = {{{up}\left( {\Delta\; s_{k}^{l}} \right)}\beta\;{f^{\prime}\left( x_{k}^{l} \right)}}}}} & (11)\end{matrix}$whereβ=(ss)⁻¹ since each element of s¹ _(k) was obtained by averaging ssnumber of elements of the intermediate output, y¹kThe inter BP of the delta error

$\left( {\Delta\;{s_{k}^{l}\overset{\Sigma}{\longleftarrow}\Delta_{i}^{l + 1}}} \right)$can be expressed as,

$\begin{matrix}{{\Delta\; s_{k}^{l}} = {\sum\limits_{i = 1}^{N_{l + 1}}{{conv}\; 1{{Dz}\left( {\Delta_{i}^{l + 1},{{rev}\left( w_{ki}^{l} \right)}} \right)}}}} & (12)\end{matrix}$where rev(.) reverses the array and conv1Dz(.,.) performs fullconvolution in 1D with K−1 zero padding. Finally, the weight and biassensitivities can be expressed as,

$\begin{matrix}{\frac{\partial E}{\partial w_{ki}^{l}} = {{{conv}\; 1{D\left( {s_{k}^{l},\Delta_{i}^{I + 1}} \right)}\mspace{20mu}\frac{\partial E}{\partial b_{k}^{l}}} = {\sum\limits_{n}{\Delta_{k}^{l}(n)}}}} & (13)\end{matrix}$As a result, the iterative flow of the BP algorithm for certainembodiments of the present invention can be illustrated in FIG. 6.

As shown in FIG. 6, certain embodiments may forward propagate an inputsignal, compute delta error at an output layer, compute the weights andbiases of the neural network while learning, and update the weights andbiases of the neural network.

In view of the above, certain embodiments of the present invention aredirected to a fast and accurate system for monitoring motor-conditionsand for performing early fault detection within motors. Certainembodiments use an adaptive implementation of 1D Convolutional NeuralNetworks (CNNs). The adaptive implementation of certain embodimentscombine the feature extraction and classification blocks of themotor-fault detection into a single learning body.

Certain embodiments of the present invention can be used in any motorand high-power engine-monitoring system. Certain embodiments can also beused in a personal computing application. Certain embodiments canperform as an automated anomaly detection and classification system forindustrial experts.

With the proposed improvements over the traditional CNNs, certainembodiments can classify each motor current signal with any samplingrate. Therefore, certain embodiments may void the need for any manualfeature extraction and may void the need for performing post processing.

FIG. 7 illustrates a flowchart of a method in accordance with certainembodiments of the invention. The method illustrated in FIG. 7 includes,at 710, receiving a signal from a motor. The signal is received whilethe motor is operating. The method includes, at 720, performing apre-processing of the signal. The method also includes, at 730,inputting the signal to a 1D convolutional neural network. The methodalso includes, at 740, detecting a fault of the motor based on theoutput of the neural network.

FIG. 8 illustrates an apparatus 10 according to another embodiment. Inone embodiment, apparatus 10 may include a neural network that performsmotor-fault detection. Although shown as a single system, thefunctionality of apparatus 10 can be implemented as a distributedsystem. Apparatus 10 includes a processor 22 for processing informationand executing instructions or operations. Processor 22 may be any typeof general or specific purpose processor. While a single processor 22 isshown in FIG. 8, multiple processors may be utilized according to otherembodiments. In fact, processor 22 may include one or more ofgeneral-purpose computers, special purpose computers, microprocessors,digital signal processors (“DSPs”), field-programmable gate arrays(“FPGAs”), application-specific integrated circuits (“ASICs”), andprocessors based on a multi-core processor architecture, as examples.

Apparatus 10 further includes a memory 14, coupled to processor 22, forstoring information and instructions that may be executed by processor22. Memory 14 may be one or more memories and of any type suitable tothe local application environment, and may be implemented using anysuitable volatile or nonvolatile data storage technology such as asemiconductor-based memory device, a magnetic memory device and system,an optical memory device and system, fixed memory, and removable memory.For example, memory 14 can be comprised of any combination of randomaccess memory (“RAM”), read only memory (“ROM”), static storage such asa magnetic or optical disk, or any other type of non-transitory machineor computer readable media. The instructions stored in memory 14 mayinclude program instructions or computer program code that, whenexecuted by processor 22, enable the apparatus 10 to perform tasks asdescribed herein.

Apparatus 10 may also include one or more antennas (not shown) fortransmitting and receiving signals and/or data to and from apparatus 10.Apparatus 10 may further include a transceiver 28 that modulatesinformation on to a carrier waveform for transmission by the antenna(s)and demodulates information received via the antenna(s) for furtherprocessing by other elements of apparatus 10. In other embodiments,transceiver 28 may be capable of transmitting and receiving signals ordata directly.

Processor 22 may perform functions associated with the operation ofapparatus 10 including, without limitation, precoding of antennagain/phase parameters, encoding and decoding of individual bits forminga communication message, formatting of information, and overall controlof the apparatus 10, including processes related to management ofcommunication resources.

In an embodiment, memory 14 stores software modules that providefunctionality when executed by processor 22. The modules may include anoperating system 15 that provides operating system functionality forapparatus 10. The memory may also store one or more functional modules18, such as an application or program, to provide additionalfunctionality for apparatus 10. The components of apparatus 10 may beimplemented in hardware, or as any suitable combination of hardware andsoftware. In one embodiment, processor 22 may be a processor thatfunctions as a neural network computing architecture. In anotherembodiment, functional modules 18 can function as a neural networkcomputing architecture.

Apparatus 10 may be configured to receive a signal from a motor. Thesignal is received while the motor is operating. Apparatus 10 may alsobe configured to perform a pre-processing of the signal. Apparatus 10may also be configured to input the signal to a 1D convolutional neuralnetwork of apparatus 10. Apparatus 10 may also be configured to detect afault of the motor based on the output of the neural network.

FIG. 9 illustrates an apparatus in accordance with certain embodimentsof the invention. Apparatus 900 may also include a receiving unit 910that receives a signal from a motor. The signal is received while themotor is operating. Apparatus 900 may also include a performing unit 920that performs a pre-processing of the signal. Apparatus 900 may alsoinclude an inputting unit 930 that inputs the signal to a 1Dconvolutional neural network. Apparatus 900 can also include a detectingunit 940 that detects a fault of the motor based on the output of theneural network.

The described features, advantages, and characteristics of the inventionmay be combined in any suitable manner in one or more embodiments. Oneskilled in the relevant art will recognize that the invention may bepracticed without one or more of the specific features or advantages ofa particular embodiment. In other instances, additional features andadvantages may be recognized in certain embodiments that may not bepresent in all embodiments of the invention.

One having ordinary skill in the art will readily understand that theinvention as discussed above may be practiced with steps in a differentorder, and/or with hardware elements in configurations which aredifferent than those which are disclosed. Therefore, although theinvention has been described based upon these preferred embodiments, itwould be apparent to those of skill in the art that certainmodifications, variations, and alternative constructions would beapparent, while remaining within the spirit and scope of the invention.

We claim:
 1. A method, comprising: receiving a signal from a motor,wherein the signal is received while the motor is operating; performinga pre-processing of the signal, wherein the pre-processing comprisesperforming a decimation preceded by an anti-aliasing filtering of thesignal to form a decimated signal; normalizing the decimated signal tohave a zero mean and to have a uniform standard deviation; linearlyscaling the normalized decimated signal into a [−1, 1] interval forminga linearly scaled signal; inputting the linearly scaled signal to a 1Dconvolutional neural network; and detecting a fault of the motor basedon an output of the 1D convolutional neural network.
 2. The methodaccording to claim 1, wherein receiving the signal comprises receivingthe signal from an induction motor.
 3. The method according to claim 1,wherein receiving the signal comprises receiving a motor current signal.4. The method according to claim 1, wherein the 1D convolutional neuralnetwork is configured to perform a plurality of back-propagationiterations, and to update weights and bias sensitivities of the 1Dconvolutional neural network for each back-propagation iteration.
 5. Themethod according to claim 1, wherein detecting the fault of the motorcomprises detecting a bearing fault of the motor.
 6. An apparatus,comprising: at least one processor; and at least one memory includingcomputer program code, the at least one memory and the computer programcode configured, with the at least one processor, to cause the apparatusat least to receive a signal from a motor, wherein the signal isreceived while the motor is operating; perform a pre-processing of thesignal, wherein the pre-processing comprises performing a decimationpreceded by an anti-aliasing filtering of the signal to form a decimatedsignal; normalize the decimated signal to have a zero mean and to have auniform standard deviation; linearly scale the normalized decimatedsignal into a [−1, 1] interval forming a linearly scaled signal; inputthe linearly scaled signal to a 1D convolutional neural network; anddetect a fault of the motor based on an output of the 1D convolutionalneural network.
 7. The apparatus according to claim 6, wherein receivingthe signal comprises receiving the signal from an induction motor. 8.The apparatus according to claim 6, wherein receiving the signalcomprises receiving a motor current signal.
 9. The apparatus accordingto claim 6, wherein the 1D convolutional neural network is configured toperform a plurality of back-propagation iterations, and to updateweights and bias sensitivities of the 1D convolutional neural networkfor each back-propagation iteration.
 10. The apparatus according toclaim 6, wherein detecting the fault of the motor comprises detecting abearing fault of the motor.
 11. A computer program product embodied on anon-transitory computer readable medium, said computer readable mediumhaving instructions stored thereon that, when executed by a computer,causes the computer to perform a method, comprising: receiving a signalfrom a motor, wherein the signal is received while the motor isoperating; performing a pre-processing of the signal, wherein thepre-processing comprises performing a decimation preceded by ananti-aliasing filtering of the signal to form a decimated signal;normalizing the decimated signal to have a zero mean and to have auniform standard deviation; linearly scaling the normalized decimatedsignal into a [−1, 1] interval forming a linearly scaled signal;inputting the linearly scaled signal to a 1D convolutional neuralnetwork; and detecting a fault of the motor based on an output of the 1Dconvolutional neural network.
 12. The computer program product accordingto claim 11, wherein receiving the signal comprises receiving the signalfrom an induction motor.
 13. The computer program product according toclaim 11, wherein receiving the signal comprises receiving a motorcurrent signal.
 14. The computer program product according to claim 11,wherein the 1D convolutional neural network is configured to perform aplurality of back-propagation iterations, and to update weights and biassensitivities of the 1D convolutional neural network for eachback-propagation iteration.
 15. The computer program product accordingto claim 11, wherein detecting the fault of the motor comprisesdetecting a bearing fault of the motor.